Prunus1350

data koch0;
    id = 1; x = cos(constant("pi") * 1 / 6); y = sin(constant("pi") * 1 / 6); density = 0; output;
    id = 1; x = cos(constant("pi") * 5 / 6); y = sin(constant("pi") * 5 / 6); density = 0; output;
    id = 1; x = cos(constant("pi") * 9 / 6); y = sin(constant("pi") * 9 / 6); density = 0; output;
run;


%macro koch(in1=, out1=, dens=);

    proc datasets library = work nolist;

Prunus1350

data kuro;
    input (var1 - var1024) ($);
cards;
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 k l 6 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 k k l o 6 0 0 0 0 0

Prunus1350

data waku;
    do y = 1 to 32;
        do x = 1 to 32;
            output;
        end;
    end;
run;

data attrmap1;
    retain id "id1"

Prunus1350

%let home_dir = /folders/myfolders/kaggle/DigitRecognizer/;

* trainingデータ読み込み ;
data train;
    infile "&home_dir.train.csv" dsd missover firstobs = 2;
    input label pixel0 - pixel783;
run;

* 描画用テンプレート作成 ;
proc template;

Prunus1350

%macro inv_matrix(in1=, out1=);

    %local i;
 
    proc contents data = &in1. out = __m1(keep=name varnum nobs) noprint;
    run;
    
    * 変数名・行数・列数をマクロ変数に格納 ;
    data _null_;
        set __m1 end = eof;

Prunus1350

%let home_dir = /folders/myfolders/kaggle/DigitRecognizer/;

* trainingデータ読み込み ;
data train;
    infile "&home_dir.train.csv" dsd missover firstobs = 2;
    input label pixel0 - pixel783;
run;

proc template;
    define statgraph digit;

Prunus1350

%macro mult_matrix(in1=, in2=, out1=);

    %local i j;

    %do i = 1 %to 2;
        proc contents data = &&in&i.. out = __m&i.(keep=name varnum nobs) noprint;
        run;
        
        * 変数名・行数・列数をマクロ変数に格納 ;
        data _null_;

Prunus1350

data rand_normal;
    do i = 1 to 1000;
        y1 = sqrt(-2 * log(rand("uniform"))) * cos(2 * constant("pi") * rand("uniform"));
        y2 = sqrt(-2 * log(rand("uniform"))) * sin(2 * constant("pi") * rand("uniform"));
        output;
    end;
run;

proc sgplot data = rand_normal;
    histogram y1;

Prunus1350

%macro pi(dim=, rep=);

    data pi;
        do i = 1 to &rep.;
            %do j = 1 %to &dim.;
                x&j. = rand("uniform") ** 2;
            %end;
            dist = sqrt(sum(of x:));
            if dist <= 1 then cnt + 1;
            calc_pi = ((cnt / i) * (2 ** &dim.) * gamma(&dim. / 2 + 1)) ** (2 / &dim.);

Prunus1350

data _null_;
    do i = 1 to 100000000;
        x = rand("uniform");
        y = rand("uniform");
        if x ** 2 + y ** 2 <= 1 then cnt + 1;
    end;
    pi = cnt * 4 / 100000000;
    put pi = ;
run;